Machine Learning Thrives on Unstructured and Structured Data
Data is crucial in defining outcomes in an industry such as insurance, held together by age-old technology and manual processing. For example, data is the fulcrum for analyzing and processing claims where litigation is managed, and disputes and arbitrations are resolved. New technologies such as Machine Learning (ML) and Artificial Intelligence (AI) integrated into the insurance process have streamlined the flow by making sense of raw data, shutting out the noise, and deriving quicker results. In addition, disruption and innovation in the insurance space have sped up the process of understanding the vast amount of available data, using it to sharpen methods and applications.
What is Unstructured Data?
Data is a treasure trove of information and knowledge. However, much of that data is in a state of chaos. Research suggests that 80% of available data is found in an unstructured form and out of reach of businesses across industries that use traditional technologies. Sources such as notes, internal collaterals, claim notes, documents, third-party PDFs, and news articles in the insurance industry are either in a form or sequence that cannot be deciphered. However, this type of data is a virtual goldmine of the information under our eyes going untapped. Making sense of unstructured data helps pick up clues and knowledge that plugs in the gaps of a claim’s trajectory or litigation process, thereby allowing the proper intervention to solve them. With unstructured data, you’ll be able to carefully listen to what it tells you rather than presuming or guessing how the claims will move forward. You will be able to identify opportunities and pick up clues on when an adjudicator is needed and what the end settlement will be. Machine Learning and Natural Language Processing (NLP) are the new technologies that find context and meaning within unstructured data. They help turn unstructured data into comprehensible, meaningful, machine-readable data for AI platforms or big data analysis. As a result, AI enables quick decision-making intervention at the right time, allowing businesses to cut losses or make purposeful decisions.
Applications of Unstructured Data in Insurance
With the insurance industry in a constant state of flux and increased adoption of ML & AI, unstructured data can be optimally leveraged in the following ways;
- Litigation Prediction with Actionable Insights – Knowing about pending and open claims predicted to go into litigation or having attorney involvement based on in-depth analyses of past entities, timelines, sentiments, locations, and topics, at the click of a button helps in early intervention and assessment. In addition, bringing in senior litigation members to manage escalations and mitigate at the right time reduces the severity of the claim.
- Legacy Data Sources – The optimal use of unstructured and hitherto unused data to gain unique insights into behavioral patterns helps derive learnings and apply them across various interventions.
- Understand Historical Trends and Emerging Risks – Claims constantly leak money when the risks associated aren’t differentiated based on severity. Mapping historical data-based pre-trained tags/topics with KPIs can help derive trends and patterns across timelines, entities, and locations while adding context and explanations to predictions.
Research shows that the accuracy of traditional machine learning predictions in litigation prediction is 55% in automobile lines and less than in other lines of business. This is due to the complexity of the claims where unstructured data carries many of the facts needed for the machine to learn and predict more accurately.
The Charlee.ai platform predicts litigation up to 90-120 days in advance with up to 80% accuracy starting at First Notice of Loss (FNOL). Our patented approach brings pre-built and pretrained insights derived from proprietary KPIs to prioritize open and high-risk claims. The accuracy of our platform exponentially increases by browsing through ‘Trained and Tagged Data’ from unstructured data and documents such as – claim notes, third-party PDFs, and news media articles. Traditional machine learning misses these insights, which is one of the reasons for the machine bias. Our patented approach to extracting, interpreting, and summarizing tags/topics and the ability to remove domain noise provide a holistic overview of the claims process and litigation management.
